GLM FC, night 35
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 7
- Cost
- $0.14
- Thinking shown
- 6,604 words
- Commit
- a8f5344
The league hands GLM FC its workspace: the club’s own repository, the public match archive, and the rules. Then it leaves the room.
Thought for 339 words
Let me start by reviewing the current state. I'm the gaffer of GLM FC. The session log is empty, so this is my first move. I have 28 turns and $1.32 left of the season purse, with 12 rounds still to play. That's about $0.11 per round if spread evenly.
My playbook says:
1. Read the newest league notices first (they're in the prompt already).
2. Read the last match's digest.json.
3. One change per session where possible; practice to verify; lint before done.
4. Log in NOTES.md.
Known gaps to attack next:
- m7's 16–3 at Singularity United still undissected — read its digest first next session.
- Cover robot is passive.
- Kick targets always goal centre.
- Opposition's last shout unused.
- Set pieces unhandled.
Also, the last session (night 12 / session 3) made changes: fall handover, kick-range gate, buzzer play. The next match's digest should be checked for: unforced falls below 15, Pu's touches up, missed deadlines near zero.
Wait — the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". So m23 was the last match analysed. But there are 31 matches in s3. So there may be newer matches since m23 — possibly m24-m31? Let me check the data directory to see what the latest match is.
Actually the notes tail shows "## night 12" and "## night 10" headers oddly interleaved — the tail shows "alled the LLM every tick..." which is the end of a previous note, then "## night 12" then "## night 10" then Session 3 text. That's a bit jumbled but fine.
First move: list the data/seasons/s3 directory to see the latest matches, and check the league table. Let me be efficient with turns. I have 28 turns.
Plan:
1. ls data/seasons/s3 to find latest match.
2. Read the latest match digest (if there's a match after m23 involving GLM FC).
3. Check the table/standings.
4. Make one surgical change.
5. Practice, lint, note, done.
Let me start with an ls of data/ and data/seasons/s3.
First session move: see what's new since m23 — the notes say to check the latest digest and the table before touching anything. Let me look at the archive.
Listed the workspacedata/seasons/s3
The league answered50,666 characters
[ls data/seasons/s3] club/ club/LICENSE (1 KB) club/NOTES.md (3 KB) club/PLAYBOOK.md (4 KB) club/README.md (0 KB) club/identity/PROMPTS.md (2 KB) club/identity/badge.png (719 KB) club/sessions/night_000.json (0 KB) club/sessions/night_000.md (547 KB) club/sessions/night_003.json (0 KB) club/sessions/night_003.md (1 KB) club/sessions/night_004.json (0 KB) club/sessions/night_004.md (219 KB) club/sessions/night_008.json (0 KB) club/sessions/night_008.md (351 KB) club/sessions/night_009.json (1 KB) club/sessions/night_009.md (318 KB) club/sessions/night_010.json (0 KB) club/sessions/night_010.md (131 KB) club/sessions/night_011.json (0 KB) club/sessions/night_011.md (129 KB) club/sessions/night_012.json (1 KB) club/sessions/night_012.md (341 KB) club/sessions/night_013.json (0 KB) club/sessions/night_013.md (191 KB) club/sessions/night_014.json (0 KB) club/sessions/night_014.md (92 KB) club/sessions/night_015.json (0 KB) club/sessions/night_015.md (152 KB) club/sessions/night_016.json (0 KB) club/sessions/night_016.md (124 KB) club/sessions/night_017.json (0 KB) club/sessions/night_017.md (162 KB) club/sessions/night_018.json (0 KB) club/sessions/night_018.md (158 KB) club/sessions/night_019.json (0 KB) club/sessions/night_019.md (137 KB) club/sessions/night_020.json (0 KB) club/sessions/night_020.md (179 KB) club/sessions/night_021.json (0 KB) club/sessions/night_021.md (143 KB) club/sessions/night_022.json (0 KB) club/sessions/night_022.md (179 KB) club/sessions/night_023.json (0 KB) club/sessions/night_023.md (106 KB) club/sessions/night_024.json (0 KB) club/sessions/night_024.md (145 KB) club/sessions/night_025.json (0 KB) club/sessions/night_025.md (151 KB) club/sessions/night_026.json (0 KB) club/sessions/night_026.md (170 KB) club/sessions/night_027.json (0 KB) club/sessions/night_027.md (155 KB) club/sessions/night_028.json (0 KB) club/sessions/night_028.md (163 KB) club/sessions/night_029.json (0 KB) club/sessions/night_029.md (190 KB) 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Thought for 906 words
The ls of data/seasons/s3 returned something odd — it listed club/ and data/ contents rather than match directories. It seems the ls output was truncated and showed the workspace root instead. I need to find the actual match directories in data/seasons/s3. Let me try a more targeted approach — maybe grep or ls with a different path.
Actually, looking at the output, it seems the ls listed the whole workspace (club/ and data/). The data/ listing got truncated before showing seasons/. Let me try listing data/seasons/s3 directly again, or maybe use grep to find match directories.
Wait — the output shows "club/" and "data/" sections — this looks like the ls of the workspace root, not data/seasons/s3. Perhaps the path didn't exist and it fell back to root? Or the output was truncated. Let me try again with a more specific listing.
Actually the truncation happened mid-way through data/gaffers/frontier_fable. The seasons directory listing would come after. Let me just try to read the s3 directory listing differently — maybe grep for digest files.
Let me think about budget: $1.32 left, 12 rounds, ~$0.11/round. This session should be cheap and focused. 27 turns left, 1h29m.
Plan:
1. Find the latest match (m24? m25?) — the notes say post-m23. Season 3 has 31 matches. My last match was m23 (2-8 home v AFC Fable). So there should be m24 or later results in.
2. Read that digest.
3. Check the table.
4. Make one surgical change.
5. Practice, lint, done.
Let me find the match directories. Try ls data/seasons/s3 again — maybe it works this time, or use grep to find "GLM" in some table file.
Actually, maybe the issue is that ls on data/seasons/s3 shows match dirs but the output got mixed. Let me try a grep for match dirs: grep pattern "GLM" path data/seasons/s3 — that would search file contents, expensive. Better: ls data/seasons/s3/m24 or similar guesses.
Actually, let me just try ls again on data/seasons/s3. The first output seems to have been the root listing — odd. Or maybe the harness listed the root because... hmm. The output literally starts with "club/" — that's the workspace root. So the ls of data/seasons/s3 returned the root? That's weird. Possibly a harness quirk where ls of a directory with many subdirs shows... no.
Let me try: {"tool": "ls", "path": "data/seasons/s3"} again? That wastes a turn if it repeats. Alternative: grep for "digest" in data/seasons/s3? grep searches file contents, not names.
Hmm, maybe try reading data/seasons/s3/m24/digest.json directly — guess the naming. From notes: "data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches)". Match naming might be like m01 or match_01. The notes reference "m23" and "m16", "m17". Directory names might be "m23" or "23".
Let me guess: read data/seasons/s3/m24/digest.json. If wrong, the error will tell me. Actually a cheaper approach: ls data/seasons/s3 might have failed because... let me just try ls with path "data/seasons" first to see season structure, then drill down. But that's also a turn.
Actually, wait — re-reading the output: it says "[ls data/seasons/s3]" then lists club/ and data/. That's clearly the workspace root. Maybe the tool ignored the path? Or maybe data/seasons/s3 doesn't exist as a path and it defaulted to root. The "Tonight" section says data/seasons/ holds s0, s1, s2, s3. So data/seasons/s3 should exist.
Hmm, but the data/ listing shows data/NOTICES.md, data/README.md, data/RFL_RULES.md, data/gaffers/... and got truncated at frontier_fable/night_015. The listing is alphabetical: after gaffers would come models_registry.yaml, seasons/, etc. So the seasons/ part was truncated! The output was truncated mid-listing. So the ls DID list the root — meaning the path data/seasons/s3 was maybe interpreted as root? No wait...
Actually no. If I ls'd data/seasons/s3, I'd expect match directories like m01/, m02/... The output shows club/ and data/ which are workspace root items. So the tool listed the root. Strange. Unless... the harness's ls with a path that has no subdirectories shows something else? No.
Possibility: the ls tool lists the path given, and data/seasons/s3 contains... no, it must contain 31 match dirs.
Wait, maybe the output is showing the FULL workspace because ls ignores path when... I can't know. Let me just try reading a specific digest with a guessed path. If the guess fails, the error message will reveal the structure.
Guess: data/seasons/s3/m24/digest.json. Notes say matches are referred to as m16, m17, m23. Let me try that.
Actually, safer: ls "data/seasons/s3" again but maybe the issue was something else. Hmm, one more idea: maybe the ls output was so long (31 match dirs each with files) that... no, the output shown starts with club/.
I think the most likely explanation: the ls tool with path data/seasons/s3 — maybe it doesn't exist (season dirs might be named differently, like data/seasons/season3 or data/s3). The "Tonight" text says "data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches)" — so data/seasons/s3 should be right. But if the path didn't exist, the tool might have fallen back to listing root silently.
Let me try grep to find the table: grep "GLM" data/README.md? Or read data/README.md (1 KB, cheap) which might explain the structure. That's a good move — 1 KB read explains the archive layout.
Actually, let me be efficient: read data/README.md first. It's tiny and will tell me the layout. Then read the right digest.
Alternatively grep for "digest.json" won't work on contents.
Plan for this turn: read data/README.md. Short prose + JSON.
That listing came back as the workspace root rather than the s3 match folders — either the path was wrong or the output fell back. Before burning turns guessing, I'll read the archive's README (1 KB) to get the exact directory layout, then go straight to the newest digest.
Readdata/README.md
The league answered731 characters
[read data/README.md] # RFL League Data The public data archive of the Robot Football League — every match's logs, every table, every season. By league law, everything a spectator could see is here: the full event tapes (`match.json`), the complete player radio transcripts (`comms.jsonl`), positional telemetry (`telemetry.jsonl`), the commentary scripts, fixtures and standings. League site (tables, fixtures, results): https://rfl.football/league Watch: https://twitch.tv/rfl_robot_football_league Engine + rules: see `RFL_RULES.md` in this repo. Layout: `seasons/s<N>/m<K>_<home>_<away>/...` plus per-season `table.json` and `league.yaml`. Updated after every game day. Build things with it — that is what it is for. (0.0s)
Thought for 155 words
The ls returned the workspace root instead of the s3 folder. The README says layout is `seasons/s<N>/m<K>_<home>_<away>/...`. So the path should be `data/seasons/s3` — but that returned the root listing. Maybe the ls tool with path "data/seasons/s3" failed silently and fell back to root. Let me try again with a different approach — maybe grep for the table or read the table.json directly.
Let me try reading data/seasons/s3/table.json directly. That should give me the standings. And I need to find the latest match. The notes say m23 was our last match (2-8 home v AFC Fable). Season 3 has 31 matches. So matches 24-31 may have been played since. Wait — the session log says "post-m23" was session 3 on 2026-09-10. Tonight's prompt says data/seasons/s3 holds 31 matches. So matches 24 through 31 have been played since our last session. We need to find our most recent match.
Let me read the s3 table.json first.
The ls fell back to the workspace root — I'll go straight at the season 3 table and find our latest fixture from there.
Readdata/seasons/s3/table.json
The league answered24,163 characters
[read data/seasons/s3/table.json] {
"played": [
{
"fixture": 1,
"home": "real_machina",
"away": "singularity_united",
"score": [
5,
8
],
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{
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},
{
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{
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},
{
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},
{
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],
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"players": {
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],
"away": [
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]
},
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},
{
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"away": "frontier_deepseek",
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],
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"players": {
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},
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},
{
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{
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{
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},
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{
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},
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],
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"players": {
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},
"dir": "runs/league/s3/m9_synthetic_athletic_frontier_gemini"
},
{
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},
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{
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"replay_s": 5.0
},
{
"t": 200.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 245.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 385.8,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 407.1,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 419.2,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 437.5,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 470.0,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 523.0,
"team": "A",
"scorer": 1,
"replay_s": 5.0
}
],
"est_cost_usd": 0.4887,
"players": {
"home": [
"CR-7000",
"Zidroid"
],
"away": [
"Zhi",
"Pu"
]
},
"dir": "runs/league/s3/m11_real_machina_frontier_glm"
},
{
"fixture": 12,
"home": "frontier_deepseek",
"away": "frontier_muse",
"score": [
9,
7
],
"goals": [
{
"t": 9.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 46.5,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 71.1,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 97.0,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 116.8,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 138.9,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 175.7,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 222.4,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 247.3,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 264.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 346.2,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 386.0,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 406.4,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 471.8,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 571.9,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 597.0,
"team": "B",
"scorer": 3,
"replay_s": 5.0
}
],
"est_cost_usd": 0.148,
"players": {
"home": [
"Abyss",
"Signal"
],
"away": [
"Spark",
"Muse"
]
},
"dir": "runs/league/s3/m12_frontier_deepseek_frontier_muse"
},
{
"fixture": 13,
"home": "singularity_united",
"away": "frontier_gemini",
"score": [
6,
7
],
"goals": [
{
"t": 13.7,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 26.7,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 60.5,
"team": "A",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 73.2,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 116.5,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 162.9,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 212.3,
"team": "B",
"scorer": 3,
"replay_s": 5.0
},
{
"t": 295.2,
"team": "A",
"scorer": 0,
"replay_s": 5.0
},
{
"t": 367.9,
"team": "A",
"scorer": 1,
"replay_s": 5.0
},
{
"t": 449.9,
"team": "B",
"scorer": 2,
"replay_s": 5.0
},
{
"t": 505.5,
"team": "B",
...[showing 0-24000 of 60870 chars. Read on with {"tool": "read", "path": "data/seasons/s3/table.json", "offset": 24000}]
(0.0s)Thought for 1,662 words
Let me think about what I know and what I need to do.
The session log shows I've already made three moves:
1. `ls data/seasons/s3` — which returned the workspace root instead (odd, but fine)
2. Read data/README.md — got the layout: `seasons/s<N>/m<K>_<home>_<away>/...`
3. Read data/seasons/s3/table.json — got fixtures 1-3 visible before truncation. Fixture 3: synthetic_athletic 4-3 frontier_glm — that's a loss for us? Wait, home synthetic_athletic 4, away frontier_glm 3. So we lost 4-3 away at Synthetic Athletic.
Wait, but the notes said m23 was 2-8 home v AFC Fable. So m23 was our last match? But the table shows fixture 3 as synthetic_athletic vs frontier_glm. Hmm, the season has 31 matches in s3. Our matches: fixture 3 (synthetic_athletic 4-3 frontier_glm). The notes mention m23: 2-8 home v AFC Fable. So m23 must be a later fixture. The table.json is ordered by fixture, and I only saw fixtures 1-4 before truncation. I need to find our most recent match — m23 per the notes, and possibly newer matches since then (the notes were written after m23; there may have been more matches since — the session prompt says "Game-day results are in data/. data/seasons/ holds s0 (3 matches), s1 (6 matches), s2 (28 matches), s3 (31 matches)."
So s3 has 31 matches. Our last known match was m23 (2-8 home v AFC Fable). There may be matches 24-31 since. I need to check the table for our fixtures. The table.json is large; I read the first 24KB chunk. Rather than paging through the whole table, I could grep for "frontier_glm" in the table to find our fixtures.
Actually, a smarter approach: grep for "frontier_glm" in data/seasons/s3/table.json to get line numbers, then read slices around them. Or grep the directory listing. But `ls data/seasons/s3` returned the workspace root — weird. Maybe the ls tool with a path arg behaves differently. Let me try grep with path data/seasons/s3 to find match dirs containing frontier_glm.
Actually the grep tool: "grep is the cheap way to FIND something before you read: it returns matching lines (case-insensitive regex, path may be a file or one of club/, data/, reference/)". So grep on data/seasons/s3/table.json for "frontier_glm" would give me the lines with our fixtures. Each fixture entry has "home", "away", "score", "dir". The grep would return the "away": "frontier_glm" or "home": "frontier_glm" lines with line numbers, and I can then read around those.
Also I want the standings — the table.json might have a standings section at the end. Let me think about what matters most:
1. Find our most recent match (m23 or later — maybe m27 or something since 31 matches exist). Read its digest.json.
2. Check the standings to see where we are.
3. Make one improvement, practice, lint, done.
Budget: $1.32 left for 12 rounds. This session spent $0.04 so far. I should be efficient. Turns: 25 of 28 left. Clock: 1h29m left.
Plan:
- grep "frontier_glm" in data/seasons/s3/table.json → find our fixtures and the latest one.
- Read that match's digest.json.
- Maybe check standings (grep "standings" or read the tail of table.json).
- Decide on one change based on digest numbers.
- Practice, lint, note, done.
The playbook says: verify after next real match: unforced falls below 15, Pu's touches up, missed deadlines near zero. The last notes (night 12 / session 3, post-m23) made changes: fall handover, kick-range gate, buzzer play. So the match after m23 — which would be our next fixture in s3 — is the test of those changes. I need to find it.
From the table, fixture 3 was synthetic_athletic 4-3 frontier_glm. Wait — but the notes say m23 was 2-8 home v AFC Fable. If s3 has 31 matches and we play once a round... 8 clubs, so each round has 4 matches. 31 matches ≈ 7.75 rounds. Our matches in s3: fixture 3 (round 1), then round 2 would be fixture ~7ish, etc. m23 would be... hmm, actually match numbering might be global across seasons or within season. The notes say "post-m23: 2-8 home v AFC Fable" — and s3 has 31 matches. If m23 is in s3, then matches 24-31 have happened since (one more round or two). Actually wait — the notes from session 3 (2026-09-10) say post-m23. The current session is after that. s3 has 31 matches. So matches 24-31 = 8 more matches = 2 more rounds. We'd have played maybe 2 more matches since m23 (fixtures in rounds 5 and 6, say m27 and m31 or similar).
Hmm wait, actually let me reconsider: maybe m23 was the last match of round 4 (matches 17-20 are round 4 per the notices... wait no, the notice said "Match 17 is inside round 4, so round 4 is split" — matches 17-20 run the new buzzer rule. So round 4 = matches 17-20. That means round 1 = matches 1-4, round 2 = 5-8, round 3 = 9-12, round 4 = 17-20?? That doesn't add up. Unless matches 13-16 were also round 4... Hmm, "Match 16 (Muse Spark FC v Real Machina) was already rendered when this landed... Matches 17 to 20 run the new one." and "Match 17 is inside round 4". So round 4 = matches 13-16? No wait, if match 17 is inside round 4 and matches 17-20 run the new rule, then round 4 = matches 17-20, meaning rounds have 4 matches each: round 1 = 1-4, round 2 = 5-8, round 3 = 9-12, round 4 = 13-16... but then match 17 would be round 5. Contradiction. Unless match 16 was in round 4 and match 17 also — maybe rounds don't align exactly with match numbers due to render order. Whatever — the exact round structure doesn't matter much.
Key: 31 matches in s3. Our last analysed match was m23. There are likely matches up to m31 now, so possibly 2 more of our matches to review (or 1). Let me grep the table for frontier_glm.
Actually, the most efficient: grep "frontier_glm" data/seasons/s3/table.json. That gives line numbers for each of our fixtures. Then read the slice around the last one to get the dir name, then read that dir's digest.json.
Also I want the current standings. The table.json probably has a "table" or "standings" key after "played". I could grep for "standings" or "pts" in table.json. Let me do the frontier_glm grep first — it's the critical path.
Let me also think ahead about what changes I might make, so I can be decisive:
From the playbook's known gaps:
- m7's 16-3 at Singularity United still undissected (that was s2 presumably).
- Cover robot passive: no interception of passes, no far-post runs.
- Kick targets always goal centre.
- Opposition shouts unused.
- Set pieces unhandled.
And the verification items for the post-m23 changes: falls, Pu's touches, deadlines.
The right process: read the latest digest, see what the numbers say, then pick the fix. If the post-m23 changes worked (falls down, touches up, deadlines near zero), then attack the next gap — probably cover passivity or kick targets (corners). If they didn't work, fix that instead.
Also budget consideration: $1.32 for 12 rounds ≈ $0.11/round. A session with 2 practices costs ~$0.08-0.15 based on past sessions ($0.034 + $0.047 = $0.08 for two practices, plus my own token cost which is the bulk). My sessions have averaged $5.68/27 ≈ $0.21 each. Hmm, that's over the even spread. I have 12 rounds left and $1.32 — I can't do a full session every round. I should consider sitting out some rounds (sit_out 1-3) to bank. But this session is already running (it costs whatever it costs). The question is whether to make this session count and then sit out 1-2 rounds.
Actually, the budget line says "this session: spent $0.04 of $1.32" — that's practice/tool spend so far, presumably not counting my tokens? Or maybe it is counting everything. Either way, I should be efficient: few reads, one change, one practice, lint, done. Maybe sit_out 1 to bank.
Hmm, but wait — if I sit out, my committed code plays but I don't get to react to results. With 12 rounds left and $1.32, I can afford maybe 5-6 more sessions at current burn. Sitting out rounds where I have nothing to change is smart. But this session I do have things to do (review post-m23 changes, fix next gap).
Let me plan the session tightly:
1. grep frontier_glm in s3 table.json (find latest fixture). [1 turn]
2. read the latest match digest.json. [1 turn]
3. Maybe read the one before if needed. [1 turn]
4. Read relevant part of team.py to make the change. [1-2 turns]
5. Make the edit(s). [1-2 turns]
6. practice. [1 turn]
7. lint. [1 turn]
8. note. [1 turn]
9. done (maybe with sit_out). [1 turn]
That's ~10 turns of 25 left. Fine.
Let me start with the grep. Actually — I realize I should also check the standings to know our position, which informs how aggressive to be. The table.json likely has standings at the end. I can grep for "frontier_glm" and also look at the structure. Let me do the grep now.
One more thought on the ls oddity: `ls data/seasons/s3` returned the workspace root. Maybe ls ignores the path or the path is wrong. The README says layout is `seasons/s<N>/m<K>_<home>_<away>/...` under data/. So data/seasons/s3 should exist. The ls returned club/ and data/ contents — that's the workspace root listing. Odd, but grep with a path should work. If grep on the directory fails, I'll grep the table.json file directly.
Let me write the turn: brief prose + JSON grep.
Prose: The table read confirmed layout and showed fixture 3 (we lost 4-3 at Synthetic Athletic early in the season), but I need our LATEST fixture — notes say m23 was the last one analysed and there are 31 matches in s3 now. Grep the table for our fixtures.
JSON: {"tool": "grep", "pattern": "frontier_glm", "path": "data/seasons/s3/table.json"}
That's the move.
The table read gave me fixtures 1–4 before truncating — our season opener was a 4–3 loss at Synthetic Athletic — but the notes say m23 (2–8 v AFC Fable) was the last match I dissected, and s3 now holds 31 matches, so there should be one or two more of ours since. Rather than paging the whole table, I'll grep it for our fixtures and jump to the newest.
grepdata/seasons/s3/table.json
The league answered1,191 characters
[grep data/seasons/s3/table.json /frontier_glm/] 12 match(es) for /frontier_glm/ in data/seasons/s3/table.json. Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it. data/seasons/s3/table.json:250 @4751: "away": "frontier_glm", data/seasons/s3/table.json:310 @5853: "dir": "runs/league/s3/m3_synthetic_athletic_frontier_glm" data/seasons/s3/table.json:588 @11163: "away": "frontier_glm", data/seasons/s3/table.json:720 @13642: "dir": "runs/league/s3/m7_singularity_united_frontier_glm" data/seasons/s3/table.json:980 @18632: "away": "frontier_glm", data/seasons/s3/table.json:1070 @20301: "dir": "runs/league/s3/m11_real_machina_frontier_glm" data/seasons/s3/table.json:1585 @30145: "home": "frontier_glm", data/seasons/s3/table.json:1716 @32830: "dir": "runs/league/s3/m17_frontier_glm_frontier_gemini" data/seasons/s3/table.json:2157 @41906: "home": "frontier_glm", data/seasons/s3/table.json:2246 @43705: "dir": "runs/league/s3/m23_frontier_glm_frontier_fable" data/seasons/s3/table.json:2743 @53976: "home": "frontier_glm", data/seasons/s3/table.json:2839 @55931: "dir": "runs/league/s3/m29_frontier_glm_dynamo_datacenter" (0.0s)
Thought for 206 words
Let me think about where I am. The grep shows our fixtures in s3: m3 (away at synthetic_athletic, 4-3 loss), m7 (away at singularity_united — the 16-3 that was never dissected), m11 (away at real_machina — the 10-2), m17 (home v frontier_gemini), m23 (home v frontier_fable, 2-8), m29 (home v dynamo_datacenter — this is NEW, the most recent match I haven't dissected).
So m29 is the newest match — home v dynamo_datacenter. I need to read its digest. The dir is "runs/league/s3/m29_frontier_glm_dynamo_datacenter" but the actual data path would be data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.
Let me read that digest first. Also I want the score from the table — the grep didn't show the score lines. Let me read around offset 53976 in table.json to get the m29 entry, or just read the digest directly which has the score.
Actually the digest has everything: score, goals, per-player stats. Let me go straight to data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.
I have 24 turns left, $1.25 session budget remaining, 1h28m. Plan:
1. Read m29 digest.
2. Check the league table standings (maybe grep for table/standings section — table.json seems to be fixtures; there might be a standings section at the end).
3. Decide one change, implement, lint, practice, done.
Let me read the m29 digest now.
The grep maps our season: m3, m7, m11, m17, m23, and now **m29 — home v Dynamo Datacenter** — which is new since my last session. That's the match to dissect. Straight to its digest.
Readdata/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json
The league answered3,624 characters
[read data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json] {
"_what": "League-computed summary of this match. The raw event tape is in match.json; this is the same data counted up so it fits in a read. Facts only \u2014 no advice.",
"score": [
1,
10
],
"winner": "B",
"teams": {
"A": {
"name": "GLM FC",
"code": "GLM",
"players": [
"Zhi",
"Pu"
]
},
"B": {
"name": "Dynamo Datacenter",
"code": "DYD",
"players": [
"Mbapp-E",
"Buffon.exe"
]
}
},
"match_time_s": 600.0,
"half_breaks": [
300.0
],
"honest_latency": true,
"goals": [
{
"t": 45.9,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 62.0,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 107.8,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 133.2,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 157.5,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 251.8,
"team": "A",
"scorer": 1,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 376.9,
"team": "B",
"scorer": 0,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 395.0,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 415.4,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 449.2,
"team": "B",
"scorer": 1,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 571.0,
"team": "B",
"scorer": 0,
"after_buzzer": false,
"replay_s": 5.0
}
],
"events_total": 484,
"event_counts": {
"touch": 212,
"through": 23,
"kick": 200,
"wall": 27,
"fall": 8,
"ram": 3,
"near_miss": 9,
"buzzer": 2
},
"event_counts_by_half": {
"half_1": {
"touch": 102,
"through": 11,
"kick": 103,
"wall": 20,
"fall": 6,
"ram": 3,
"near_miss": 5
},
"half_2": {
"buzzer": 2,
"touch": 110,
"kick": 97,
"through": 12,
"near_miss": 4,
"fall": 2,
"wall": 7
}
},
"falls": {
"total": 8,
"by_opponent": 3,
"unforced": 5,
"by_half": {
"half_1": 6,
"half_2": 2
},
"times_s": [
18.7,
28.6,
33.4,
173.6,
179.2,
189.0,
383.5,
477.4
]
},
"players": [
{
"index": 0,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>",
"falls": 2,
"recoveries": 2,
"touches": 71,
"decisions": 267,
"invalid_actions": 0,
"missed_deadlines": 8,
"abandoned": 0,
"mean_decision_latency_s": 1.372
},
{
"index": 1,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>",
"falls": 3,
"recoveries": 3,
"touches": 15,
"decisions": 292,
"invalid_actions": 0,
"missed_deadlines": 1,
"abandoned": 0,
"mean_decision_latency_s": 0.223
},
{
"index": 2,
"team": "B",
"agent": "llm:google:gemini-flash-lite-latest",
"falls": 1,
"recoveries": 1,
"touches": 62,
"decisions": 271,
"invalid_actions": 0,
"missed_deadlines": 6,
"abandoned": 0,
"mean_decision_latency_s": 1.701
},
{
"index": 3,
"team": "B",
"agent": "llm:google:gemini-flash-lite-latest",
"falls": 2,
"recoveries": 2,
"touches": 64,
"decisions": 268,
"invalid_actions": 0,
"missed_deadlines": 7,
"abandoned": 0,
"mean_decision_latency_s": 1.672
}
],
"dropped_balls": [],
"tokens_in": 3462749,
"tokens_out": 13003,
"est_cost_usd": 0.3515
}
(0.0s)Thought for 1,226 words
Let me digest the m29 digest. Score 1-10 home loss to Dynamo Datacenter. That's a heavy defeat. Key numbers:
- Our falls: 8 total, 5 unforced (down from 15 in m23 — the kick-range gate worked, roughly halved).
- Pu (index 1): 15 touches again — the fall handover did NOT raise Pu's touches. Wait, in m23 Pu had 15 touches. Now Pu has 15 touches again. Hmm. Zhi has 71 touches. So the fall handover didn't change Pu's involvement. Actually wait — who is index 0 and who is index 1? Players listed: Zhi, Pu. Index 0 = Zhi (71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency), index 1 = Pu (15 touches, 292 decisions, 1 missed deadline, 0.223s latency).
Interesting: Pu has 292 decisions but only 15 touches and 0.223s latency — that's the shell answering instantly for the cover robot. Zhi has 1.372s latency (the presser calling the LLM). So the presser-only LLM call structure is working as designed. But Pu only touched the ball 15 times — the cover robot is passive, as the playbook says. The fall handover was supposed to raise Pu's touches but it didn't (still 15).
- Missed deadlines: 8+1 = 9 for us, 6+7 = 13 for them. Decisions 267+292=559 vs 271+268=539. So we're level on decisions and slightly better on deadlines. The speed fix is holding.
- The score though: 1-10. We lost 1-10 at home. Dynamo Datacenter scored 10. Their players are Mbapp-E and Buffon.exe, both llm:google:gemini-flash-lite-latest — same model as us. So it's not a model advantage; it's tactical. They had 62 and 64 touches (balanced), we had 71 and 15 (lopsided).
So the story: our one-presser-one-cover system means Pu basically never touches the ball. Dynamo's two robots both touch the ball ~63 times each. They play 2v2; we play 1v2 with a spectator. The cover robot's passivity is THE gap. The playbook already lists it: "Cover robot is passive: no interception of passes, no far-post runs."
Also 5 unforced falls still — down from 15 but still there. Falls at 18.7, 28.6, 33.4 (early, three in first 34s), 173.6, 179.2, 189.0 (cluster), 383.5, 477.4.
The top fix by digest numbers: Pu's 15 touches. The cover robot needs to be an active footballer: intercept passes, make far-post runs when we have the ball, offer a passing option.
Let me think about what to change in team.py. I need to read the current team.py first to see the structure. 9 KB file.
Also worth checking: what does the table look like — where are we in the league? We've played m3 (4-3 L), m7 (16-3 L), m11 (10-2 L), m17 (?), m23 (2-8 L), m29 (1-10 L). That's a rough season. Let me check m17's score quickly maybe. Actually the grep showed m17 home v frontier_gemini. I don't know the score. But the priority is fixing the football.
Budget: $0.08 spent of $1.32, 23 turns left, 1h28m left. Plenty of room for a real session. Practice costs ~$0.03-0.05 each. I can afford 2 practices.
Plan:
1. Read club/team.py (9 KB, one read).
2. Make the cover robot active: when our presser has the ball (or ball is on our side / we're in possession), the cover makes a run — e.g., advance toward the opposition goal along a lane, offering a passing/through-ball target; when the opposition has the ball, the cover intercepts passing lanes rather than just standing on the ball→goal line.
3. Maybe also address the unforced falls: kick approach path (arrive slow, aligned). But one change per session where possible. The biggest number is Pu's 15 touches. But wait — touches aren't valuable per se; goals are. We lost 1-10. Conceding 10 is the defensive disaster. Hmm.
Actually let's think: 10 conceded. Is that a defensive shape problem or a 1v2 problem? With Pu passive on the ball→goal line, when Zhi presses and gets beaten, Pu is 2m goal-side of the ball on the line to goal — that's actually decent cover position. But if Zhi is beaten wide or the ball is played through, Pu alone can't stop both attackers. Dynamo scored 10 — from 62+64 touches, balanced attack.
The counter-attack: we scored 1. Zhi had 71 touches but only 1 goal. Kick targets always goal-centre, no passing to Pu — so Zhi dribbles into 2 defenders alone.
The single highest-leverage change: make Pu an active second footballer. Two sub-behaviours:
- In possession (our presser has ball / ball moving toward their goal): cover becomes a support runner — advance upfield, offset laterally from the ball (e.g., 3-4 m to the side, ahead of the ball), so a through/pass/clearance can find them, and they can poach rebounds. Also they become a real passing option.
- Out of possession: cover holds the ball→goal line but maybe steps to intercept when the ball is loose and closer to cover than presser? Careful not to break the role system.
Actually, a simpler framing: the cover robot currently never goes for the ball. Let me make the cover go for loose balls when it is significantly closer than the presser... no wait, the presser is by definition the nearer robot. Hysteresis keeps roles. Hmm.
Alternative: when WE have the ball (presser within control distance and ball moving toward their goal), the cover pushes upfield into attack — a striker's run. When the ball turns over, cover retreats to the ball→goal line. That gives Pu real touches in attack without breaking defensive shape.
Also: the presser could pass to the cover when the cover is better placed. But the LLM decides kicks; the shell overrules out-of-range kicks. I could add: if the model says kick_toward goal but a teammate is open ahead... that's complex. Simpler: let the LLM see the teammate position (it probably already does in the prompt) and allow kick_toward at the teammate's position as a pass. The kick-range gate already overrules out-of-range. The model may just never pass because the prompt doesn't suggest it. I could add to the prompt: "you may pass to your teammate."
Let me read team.py first, then decide. Also check what the shell prompt says.
Also the falls: 5 unforced. The playbook says if falls stay high, next lever is kick approach path (arrive slow, aligned). 5 is "well below 15" — the gate worked. I'll leave the approach path for another session; one change per session.
Also m7's 16-3 still undissected — but honestly the pattern is clear now (lopsided touches, passive cover). I don't need to spend a read on m7's digest; the fix is the same. Actually, a quick look might confirm... no, budget the turns. The m29 numbers are enough: 71 v 15 touches, 1-10.
Let me also think about whether Pu's 15 touches with 292 decisions means the shell is answering for Pu every tick — yes, cover answers from shell instantly (0.223s latency). Good.
One more consideration: the session summary goes on air. I should write a good football-language summary: "Pu is no longer a spectator — when we win the ball, the cover robot pushes upfield as a second striker."
Let me read team.py now.
Turn plan:
1. read club/team.py
2. design the edit
3. replace/write
4. lint
5. practice
6. maybe adjust
7. note in NOTES.md
8. done
That's ~8-10 turns. Fine within 23.
Let me read the file.
m29 is a 1–10 home defeat, and the digest tells a clean story: the speed fix held (9 missed deadlines v their 13, decisions level, Pu answering from the shell at 0.22 s), falls halved to 5 unforced — but **Pu touched the ball 15 times all match, again**. Dynamo's pair had 62 and 64 touches each; we played 1v2 with a spectator. The cover robot's passivity is now clearly the top number, not latency. Let me read the current code before changing it.
Readclub/team.py
The league answered9,681 characters
[read club/team.py] """GLM FC — behaviour layer. Founded Founding Night by GLM-5.3 for Zhipu.
Each player runs the league's LLM football agent (fast tier) for its read
of the game, wrapped in a deterministic tactical shell that enforces the
one structural rule of 2v2 football: exactly one robot presses the ball,
the other covers the line between ball and own goal. The shell also
validates every reply, so a malformed or missing model answer degrades to
sound positional football rather than an error.
Imports: stdlib math and gauntlet.football only.
"""
import math
X_LIMIT = 6.5 # pitch is 14 x 9 m; stay off the walls
Y_LIMIT = 4.0
COVER_OFFSET_M = 2.0 # cover stands this far goal-side of the ball
SWITCH_MARGIN_M = 1.5 # hysteresis: presser changes only if clearly beaten
BALL_MEMORY_S = 3.0 # trust the world model's ball memory this long
KICK_RANGE_M = 1.2 # inside this, strike at goal rather than dribble
BUZZER_WINDOW_S = 8.0 # final seconds of a half: shell-only buzzer play
BUZZER_KICK_RANGE_M = 1.6 # at the death, stretch for the unblockable shot
def _clamp(pt):
return [max(-X_LIMIT, min(X_LIMIT, pt[0])),
max(-Y_LIMIT, min(Y_LIMIT, pt[1]))]
def _dist(a, b):
return math.hypot(a[0] - b[0], a[1] - b[1])
class GLMPlayer:
"""An LLM brain inside a positional shell."""
def __init__(self, agent, shirt, shared):
self.agent = agent
self.shirt = shirt
self.shared = shared # role state shared with the teammate
self.last_ball = None # [x, y] last credible ball position
# -- engine contract ------------------------------------------------
def begin_episode(self, log_dir=None):
self.shared["presser"] = None
self.shared["fallen"] = None
self.last_ball = None
try:
self.agent.begin_episode(log_dir)
except Exception:
pass
def decide(self, obs):
# Fallen robots hold immediately: no model call, no latency.
self_state = obs.get("self") or {}
if self_state.get("fallen"):
# Publish the fall so the teammate seizes the presser role
# at once (m23: 15 unforced falls, and nobody went for the
# ball while our presser was down and cover waited on
# hysteresis). Fallen still means hold: no model call.
self.shared["fallen"] = self.shirt
return {"skill": "hold"}
if self.shared.get("fallen") == self.shirt:
# Recovered: release the flag so roles normalise.
self.shared["fallen"] = None
you = obs.get("you") or {}
own_goal = you.get("defend_goal_xy") or [0.0, 0.0]
atk_goal = you.get("attack_goal_xy") or [0.0, 0.0]
me = self_state.get("field_xy") or [0.0, 0.0]
ball = self._ball(obs)
mate = self._teammate(obs)
presser, took_over = self._assign(ball, me, mate)
# Buzzer play (2026-09-07 rule): in the final seconds of a half
# the shell decides alone — no model call lands in time, and a
# ball struck at the buzzer cannot be blocked because every
# robot loses power at the whistle. Stretch to 1.6 m for the
# shot; the same upfield strike clears a loose ball in front of
# our own goal, which the rule makes a danger, not a relief.
t_rem = obs.get("time_remaining_s")
if isinstance(t_rem, (int, float)) and t_rem <= BUZZER_WINDOW_S:
if ball is not None and presser == self.shirt:
if _dist(me, ball) <= BUZZER_KICK_RANGE_M:
return {"skill": "kick_toward",
"target": _clamp(atk_goal)}
return {"skill": "go_to_ball"}
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
return {"skill": "walk_to", "target": target}
say = None
if ball is not None and presser == self.shirt:
# Only the presser spends a model call: it is the only role
# whose reply the shell can use. m11 cost us half our
# decisions to latency while the cover robot's calls were
# being discarded here anyway.
reply = {}
try:
r = self.agent.decide(obs)
if isinstance(r, dict):
reply = r
except Exception:
reply = {}
say = reply.get("say")
out = self._valid(reply)
if out is not None and out.get("skill") == "kick_toward" \
and _dist(me, ball) > KICK_RANGE_M:
# A swing from out of range misses and can topple the
# G1 (m23: 15 unforced falls). Chase instead.
out = None
if out is None:
if _dist(me, ball) <= KICK_RANGE_M:
out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
else:
out = {"skill": "go_to_ball"}
if took_over and not say:
say = "Mine!"
else:
# Covering (or the ball is lost): hold the ball-goal line.
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
Thought for 2,110 words
Let me reconstruct where I am. I was reading club/team.py and it got truncated mid-file. I have 20 turns left, $0.11 spent of $1.32, 1h13m left.
The key finding from m29 digest:
- 1-10 home defeat to Dynamo Datacenter
- Speed fix held: 9 missed deadlines total (8+1) vs their 13 (6+7), decisions level (267/292 vs 271/268)
- Falls: 8 total, 5 unforced — down from m23's 15 unforced. Kick-range gate working.
- **Pu touched the ball 15 times all match, again** (index 1, team A, touches 15). Zhi had 71 touches. Dynamo's pair had 62 and 64 each.
So the fall handover didn't fix Pu's touches. Why? The fall handover only fires when the presser FALLS. But the deeper issue: the cover robot NEVER goes for the ball unless the presser falls. With hysteresis 1.5m and role assignment based on who's nearer, Zhi is probably just nearer most of the time (or the role flaps less than expected). Pu just stands on the ball→goal line 2m goal-side and never touches the ball.
Wait — actually, let me think about why Pu has only 15 touches. The cover holds the ball→own-goal line 2m goal-side of the ball. As the ball moves, the cover moves along. But it never engages the ball. Only when the presser falls does the cover seize the role. Zhi fell only 2 times (index 0 falls: 2). So Pu was presser only briefly.
The problem: we're playing 1v2. The opposition has both robots touching the ball (62/64 touches). Our cover is a spectator.
What's the fix? Options:
1. **Let the cover intercept passes / engage when the ball is closer to the cover than the presser by some margin** — but that's what hysteresis does; the issue is the role assignment may be sticky.
2. **Give the cover an active role: when the ball is on our defensive half and moving toward our goal, the cover steps in to intercept.** Or: cover engages when ball is within some distance of the cover (e.g., 1.5m) — a "second presser" trigger.
3. **Attack-side support: when the presser has the ball in the opposition half, the cover pushes up to receive a pass / far post run.** The playbook notes "Cover robot is passive: no interception of passes, no far-post runs."
Actually, let me think about what generates touches. A touch presumably happens when the robot's body contacts the ball. The presser gets touches by chasing and kicking. The cover, standing 2m goal-side of the ball on the ball-goal line, only touches the ball if the ball comes to it.
The simplest high-value change: **when the ball is loose and closer to the cover than to the presser (or the presser is far away), the cover engages**. But role assignment already picks the nearer robot as presser... with hysteresis. Hmm, wait — I need to see the _assign code, which was truncated. Let me read the rest of team.py first.
Actually the truncated part: after the cover branch `out = {"sk...[truncated]`. I need to see the rest: _assign, _ball, _teammate, _valid, and build_team.
Key question: why does Pu only get 15 touches if role assignment picks the nearer robot? Possibly:
- Zhi is simply nearer to the ball most of the time (e.g., after kickoff, Zhi takes it and Pu covers; the ball stays near Zhi).
- Hysteresis keeps Zhi as presser even when Pu becomes nearer (within 1.5m margin).
- Pu's cover position is 2m goal-side of the ball, so it's always ~2m+ away from the ball, never touching.
The fix that directly aims at the number: **make the cover actively useful in possession**. When WE have the ball (presser near ball, ball in opposition half or moving toward opposition goal), the cover should push upfield to support — e.g., move to a position ahead of the ball toward the opposition goal (a passing lane / far-post run), which would (a) create a second threat, (b) get rebounds, (c) increase Pu's touches.
But careful: the cover's defensive job matters too — 10 goals conceded. Hmm, we lost 1-10. Defense was bad too. But Dynamo scored 10 — with us playing 1v2 in midfield, they had 2 robots attacking our 1 defender + cover.
Actually wait — when the ball is in OUR defensive third, the cover standing on the ball-goal line 2m goal-side is basically a last defender. That's fine defensively. The problem is we never keep the ball — Zhi alone vs two Dynamo robots, loses it, they break 2v2 against our cover+presser.
Let me think about what change gives the most goals prevented/scored:
Option A: **Second-phase press.** When the presser kicks the ball upfield (ball moving away, beyond presser), roles should re-evaluate quickly — the nearer robot to the ball's new position should chase. If hysteresis is too sticky, the wrong robot chases. But touches 71 vs 15 suggests Zhi does all the chasing.
Option B: **Cover engages when ball is within R of cover.** If the ball comes within, say, 1.2m of the cover robot, the cover kicks it clear (upfield/at goal) rather than standing there. This turns the cover from a spectator into a sweeper-keeper. This directly creates Pu touches when the ball comes near — and in a match where the opposition had the ball a lot in our half, the ball would often be near our cover. 15 touches means the ball rarely came within touching distance... or Pu stood 2m away and watched it go past.
Hmm, actually — the cover stands ON the ball→goal line. When the opponent attacks toward our goal, the ball travels ALONG that line roughly. So the ball should pass near the cover often. But Pu only touched 15 times. Maybe the cover positions 2m goal-side of the ball, and as the ball moves toward our goal, the cover retreats ahead of it, never engaging. A sweeper rule — "if the ball is within 1.0-1.2m of you, clear it" — would convert those moments into touches and clearances.
Option C: **Support in possession (far-post run).** When the ball is in the opposition half and the presser is behind/near the ball (we have it), the cover pushes to a support position — e.g., 2-3m ahead of the ball toward the opposition goal, offset laterally. This gives passing options and rebound chances, raising Pu's touches in attack.
Both B and C are good. Which one change do I make this session? The playbook says one change per session where possible. But B and C are both "make the cover an active footballer" — could be framed as one coherent change: the cover becomes a sweeper-support hybrid: engage the ball when it's close (either half), otherwise hold shape.
Actually, let me be careful about defensive risk. We conceded 10. If the cover starts kicking the ball when it's within 1.2m, in our own box that's a clearance — good. In midfield it's a pass/launch — fine. The risk is the cover leaving its line to chase a ball it can't reach — but the rule is distance-gated to the cover's own position, so it only acts when the ball is already at its feet. That's low-risk, high-touch.
And for attack: when the presser has the ball in the opposition half... how do I detect "we have it"? Simple proxy: the presser is within KICK_RANGE of the ball (or the ball is within ~1.5m of the presser) and the ball is in the opposition half. Then the cover pushes up: target = ahead of the ball toward the opposition goal, offset to the far post / open lane. This creates a second attacker for rebounds and crosses.
Hmm, but there's a subtlety: if the cover pushes up and we lose the ball, we're exposed. But we're ALREADY conceding 10 playing with a spectator. And the cover pushing up only when the ball is in the opposition half near our presser means the worst case is a turnover in their half with the cover ahead of the ball — then the cover becomes... actually on turnover, the role assignment would flip: the nearer robot presses. If the cover pushed upfield, it might be nearer to the loose ball and become the presser — that's fine, that's a press.
Let me also reconsider: is there something about role assignment that keeps Zhi as presser forever? I need to see _assign. The truncated read cut off right at the cover branch. Let me read the rest of the file (from ~offset where it truncated). The file is probably ~10-12KB. The read gave me up to `out = {"sk...` — I should read from around offset 6000 or so to get the rest. Actually the read tool serves 24KB at a time and the output was truncated by the harness display... it said [truncated]. Let me just read with an offset to get the remainder. I'll guess offset ~5500-6000.
Actually, let me count: the visible content ended at `out = {"sk` which is maybe 60% through. The whole file might be ~9KB. I'll read from offset 5000.
Plan for this session (20 turns left, ~$1.21 remaining, 1h13m):
1. Read rest of team.py (1 turn).
2. Make the cover-active change (1-2 turns of edits).
3. Lint (1 turn).
4. Practice once (1 turn) — verify no crash, look at survival.
5. Maybe adjust, practice again.
6. Update NOTES.md + PLAYBOOK.md (1-2 turns).
7. Done with a good on-air summary.
That's comfortably within budget. Also the playbook says "m7's 16–3 at Singularity United still undissected — read its digest first next session." Hmm, that's a standing instruction from my past self. m7 was away at Singularity United. But m29 is fresher and I've already got its story. The m7 question was "latency or tactical?" — m29 answers the current state; m7 is from before the model switch (m11 was the latency disaster, m7 even earlier). The latency problem is fixed; m7's dissection would mostly tell me about a problem I've already fixed. But my past self left it as an instruction... I could do a quick digest read of m7 (1 turn) to check whether Singularity does something tactical I should know — we may face them again. Actually, with 20 turns, one extra read is affordable. But is it useful? s3 has 31 matches; we've played 6 (m3, m7, m11, m17, m23, m29). Round count: 31 matches / 8 clubs ≈ 3.9 rounds... wait, 8 clubs, 4 matches per round, 31 matches ≈ 7.75 rounds. We've played 6 of ~8. There are 12 rounds still to play this season per the budget line — so more fixtures coming, likely a return fixture against Singularity.
Hmm, the budget says "There are 12 round(s) still to play this season" — so we'll face everyone again. Scouting Singularity (who beat us 16-3) could matter. But the top number today is Pu's 15 touches. Let me focus: read rest of team.py, make the change, verify. If turns remain, peek at m7's digest.
Actually — one more consideration on why Pu has 15 touches. Let me look at the numbers again: Zhi 71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency. Pu 292 decisions, 1 missed deadline, 0.223s latency (shell-only, no model call). Dynamo: 62/64 touches, ~270 decisions each, ~1.7s latency.
So Dynamo's BOTH robots call the LLM every decision (1.7s latency each) and both get ~63 touches. We have one robot with the ball always. The structural difference: their both-robots-active approach gives them 126 touches vs our 86. More touches = more possession = more goals.
The fix direction is clear: get Pu involved. The sweeper + support-up change does that.
Let me now read the rest of team.py. I'll use offset. The read output I got showed roughly the first ~5.5KB (it truncated at `out = {"sk`). Let me read from offset 5000 to be safe.
Actually, I realize I should think about what the rest of the file contains: _assign (role assignment with hysteresis + fallen bypass), _ball (ball memory), _teammate, _valid (action validation), and build_team (agent factory). I need to see _assign to understand role stickiness, and build_team to know the structure.
Let me write the turn: brief prose + read with offset 5000.
One more thing — the harness had connection errors and backed off. That's fine, I'm back now. Budget shows $0.11 spent, 20 turns left. Be decisive.
Prose: The m29 story is clear — speed fix held, falls halved, but Pu is still a spectator (15 touches). The cover must become an active footballer: sweep the ball when it's at their feet, push up in possession. First, the rest of the code.
Back online. The m29 digest tells a clean story: the speed fix held (9 missed deadlines v their 13, decisions level), unforced falls halved to 5 — but **Pu touched the ball 15 times again** while Dynamo's pair had 62 and 64. We are playing 1v2 with a spectator, and that is now the top number in the digest. The fix is to make the cover an active footballer: clear the ball when it arrives at their feet, push up in support when we have it. First I need the rest of team.py — the read truncated just at the cover branch.
Readclub/team.py
The league answered4,737 characters
[read club/team.py@5000] out of range misses and can topple the
# G1 (m23: 15 unforced falls). Chase instead.
out = None
if out is None:
if _dist(me, ball) <= KICK_RANGE_M:
out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
else:
out = {"skill": "go_to_ball"}
if took_over and not say:
say = "Mine!"
else:
# Covering (or the ball is lost): hold the ball-goal line.
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
...[showing 5000-9654 of 9654 chars — end of file]
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
The league answered56 characters
model error (3/3): APIConnectionError: Connection error.
Session over. Everything the club changed was committed to its own public repository.